A Nomogram for Prediction of Complications Based on TM&M System of VATS Major Lung Surgery for Lung Cancer.
10.3779/j.issn.1009-3419.2021.103.12
- Author:
Ke LAN
1
;
Jian ZHOU
1
;
Haihua GUO
2
;
Yunfeng NI
2
;
Fan YANG
1
Author Information
1. Department of Thoracic Surgery, Peking University People's Hospital, Beijing 100044, China.
2. Department of Thoracic Surgery, Tang Du Hospital, Air Force Military Medical University, Xi'an 710038, China.
- Publication Type:Journal Article
- Keywords:
Complication;
Lung neoplasms;
Prediction model;
Video assisted thoracoscopic surgery
- MeSH:
Aged;
Humans;
Lung;
Lung Neoplasms/surgery*;
Middle Aged;
Morbidity;
Nomograms;
Pneumonectomy;
Postoperative Complications/etiology*;
Retrospective Studies;
Thoracic Surgery, Video-Assisted
- From:
Chinese Journal of Lung Cancer
2021;24(12):838-846
- CountryChina
- Language:Chinese
-
Abstract:
BACKGROUND:Postoperative complications are an important cause of death after lung resection. At present, the adoption of video assisted thoracoscopic surgery (VATS) for lung cancer in China is increasing every year, but the prediction model of postoperative complications of VATS for lung cancer is still lack of evidence based on large sample database. In this study, Thoracic Mortality and Morbidity (TM&M) classification system was used to comprehensively describe the postoperative complications of VATS major lung resection in our center, and the prediction model of complications was established and verified. The model can provide basis for the prevention and intervention of postoperative complications in such patients, and accelerate the recovery of patients.
METHODS:The clinical data of patients underwent VATS major lung resection in our center from January 2007 to December 2018 were collected retrospectively. Only patients with stage I-III lung cancer were included. The postoperative complications were registered strictly by TM&M classification system. The patients were divided into two groups according to the operation period: the early phase group (From 2007 to 2012) and the late phase group (From 2013 to 2018). The baseline data of the two groups were matched by propensity score matching. After matching, binary logistic regression analysis was used to establish the prediction model of complications, and bootstrap internal sampling was used for internal verification.
RESULTS:A total of 2,881 patients with lung cancer were included in the study, with an average age of (61.0±10.1) years, including 180 major complications (6.2%). Binary Logistic regression analysis of 1,268 matched patients showed: age (OR=1.04, 95%CI: 1.02-1.06, P<0.001), other period (OR=0.62, 95%CI: 0.49-0.79, P<0.001), pathological type (OR=1.73, 95%CI: 1.24-2.41, P=0.001), blood loss (OR=1.001, 95%CI: 1.000-1.003, P=0.03), dissected lymph nodes (OR=1.022, 95%CI: 1.00-1.04, P=0.005) were independent risk factors for postoperative complications. The ROC curve indicates that the model has good discrimination (C-index=0.699), and the C-index is 0.680 verified by bootstrap internal sampling for 1,000 times. The calibration curve shows a good calibration of the prediction model.
CONCLUSIONS:TM&M system can comprehensively and accurately report the postoperative complications of thoracoscopic lung cancer surgery. Age, operative period, pathological type, intraoperative bleeding and dissected lymph nodes were independent risk factors for postoperative complications of VATS major lung resection for lung cancer. The established complication prediction model has good discrimination and calibration.
